CSC 402-1
Gonzalo Mateos Buckstein
MW 3:25PM - 4:40PM
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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites: Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required.
- Location
- Computer Studies Room 601
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CSC 404-1
Michael Huang
WF 3:25PM - 4:40PM
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This course provides in-depth discussions of the design and implementation issues of multiprocessor system architecture. Topics include cache coherence, memory consistency, interconnect, their interplay and impact on the design of high-performance micro-architectures. prerequisite ECE 200 or CSC 252 or permission of instructor
- Location
- Hylan Building Room 101
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CSC 414-1
Mujdat Cetin; Chenliang Xu; Ross Maddox
MW 10:25AM - 11:40AM
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This is the second course offered as part of the PhD training program on augmented and virtual reality. It builds on the first course, Introduction to Augmented and Virtual Reality (AR/VR). The goal of the course is to provide exposure to problems in the AR/VR domain addressed by various disciplines. The course consists of three one-month long modules in a semester. Modules engage students in particular aspects of AR/VR or hands-on experience on AR/VR. Modules to be offered in various years include: fundamentals of optics for AR/VR; AR/VR in the silicon; foundations of visual perception in the context of AR/VR; computer audition and acoustic rendering; measuring the human brain; deep learning and visual recognition for AR/VR; brain-computer interfacing in a virtual environment; 3D interfaces and interaction; AR/VR for collaborative education & professional training. In Spring 2023, the following three modules will be offered: 1) Deep learning and visual recognition for AR/VR. (Prof. Chenliang Xu) Recent developments in deep learning have significantly advanced state-of-the-art visual recognition in problems such as object detection, activity recognition, and semantic segmentation. In this module, students will learn core concepts in deep learning, including convolutional neural networks and recurrent neural networks. They will receive hands-on experience using popular deep-learning libraries like PyTorch to build visual recognition algorithms for AR/VR systems. 2) Measuring the human brain. (Prof. Ross Maddox) This module will introduce students to studies of human brain function using non-invasive methods. It will focus on experimental paradigms and data analysis in the time and frequency domains. Neural encoding and decoding models and applications to brain-computer interfaces will also be discussed. The module will have a mixed format, with lectures and labs. Lab exercises will be based on analyzing real data from human subjects. 3) Professional encounters with leading AR/VR researchers. This module will involve a series of seminars and discussion sessions with leading AR/VR researchers from academia and industry. Prerequisites: ECE 410 or OPT 410 or BME 410 or NSCI 415 or CSC 413 or CVSC 534 INSTRUCTORS: Chenliang Xu; Ross Maddox; Mujdat Cetin
- Location
- Todd Union Room 202
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CSC 440-1
Thaddeus Pawlicki
TR 4:50PM - 6:05PM
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Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project.
- Location
- Dewey Room 1101
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CSC 443-2
Ralf Haefner
MW 3:25PM - 4:40PM
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This is a seminar-style course for advanced undergraduate and graduate students covering multiple areas of computational neuroscience by weekly readings and student presentations. Many of the topics are deeper explorations of topics covered in Introduction to Computational Neuroscience, focusing on the sensory system, decision-making, action selection and active inference, especially from a probabilistic and normative perspective. The reading list is somewhat flexible and adaptable to student interest. There is an opportunity for a final project but this is not required. Prerequisite: CSC 241/441 or permission of instructor
- Location
- Meliora Room 324
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CSC 446-1
Adam Purtee
TR 11:05AM - 12:20PM
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Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms. No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended.
- Location
- Gavett Hall Room 202
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CSC 447-1
Hangfeng He
MW 3:25PM - 4:40PM
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This course addresses issues of representing computing meaning from natural language, especially issues of understanding language in context using commonsense knowledge of the world. Topics will include a survey of English phrase structure and parsing, semantic representation (e.g., events, semantic roles, time, causality and speech acts), and theories and techniques for understanding language in context, including intention recognition, text understanding using knowledge of scripts and plans, and models of spoken dialogue systems (e.g., conversational agents such as Siri). CSC447, the graduate level version of the course, requires a substantial individual project. Prerequisites: CSC 242
- Location
- Computer Studies Room 209
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CSC 449-1
Jiebo Luo
TR 9:40AM - 10:55AM
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Introduction to computer vision, including camera models, basic image processing, pattern and object recognition, and elements of human vision. Specific topics include geometric issues, statistical models, Hough transforms, color theory, texture, and optic flow. CSC 449, a graduate-level course, requires additional readings and assignments. Prerequisites: MATH 161 and CSC 242; MATH 165 strongly recommended
- Location
- Gavett Hall Room 202
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CSC 450-1
Scott Grimm
TR 11:05AM - 12:20PM
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This course addresses linguistic research questions through data science techniques. The course will focus on developing skills to (i) acquire and process a variety of language data, from using established corpora to capturing Twitter feeds, and (ii) to investigate language use, particularly syntactic and semantic phenomena, through descriptive and inferential statistical techniques. A significant part of the course will be devoted to hands-on projects and will include developing familiarity with using the programming languages Python and R to acquire and explore linguistic data. Familiarity with statistics and/or computational linguistics is advantageous, but not necessary.
- Location
- Hylan Building Room 203
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CSC 452-1
Yuhao Zhu
WF 3:25PM - 4:40PM
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/Obuses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required.
- Location
- Gavett Hall Room 206
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CSC 455-1
Sreepathi Pai
MW 10:25AM - 11:40AM
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Programming is the automation of information processing. Program analysis and transformation is the automation of programming itself---how much a program can understand and improve other programs. Because of the diversity and complexity of computer hardware, programmers increasingly depend on automation in compilers and other tools to deliver efficient and reliable software. This course combines fundamental principles and (hands-on) practical applications. Specific topics include data flow and dependence theories; static and dynamic program transformation including parallelization; memory and cache management; type checking and program verification; and performance analysis and modeling. The knowledge and practice will help students to become experts in software performance and correctness. Students taking the graduate level will have additional course requirements and a more difficult project.
- Location
- Gavett Hall Room 301
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CSC 458-1
Chen Ding
MW 3:25PM - 4:40PM
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Principles of parallel and distributed systems, and the associated implementation and performance issues. Topics covered will include programming interfaces to parallel and distributed computing, interprocess communication, synchronization, and consistency models, fault tolerance and reliability, distributed process management, distributed file systems, multiprocessor architectures, parallel program optimization, and parallelizing compilers. Students taking this course at the 400 level will be required to complete additional readings and/or assignments.
- Location
- Dewey Room 2110E
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CSC 460-1
Ehsan Hoque
W 2:00PM - 4:40PM
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We currently add 51 billion tons of greenhouse gases to the atmosphere every year. We need to get it down to zero to stop the warming and avoid a cloud disaster. How? In this class, we will discuss and explore the role of technology to bring innovative breakthroughs to combat this unique challenge. Examples include the use of machine learning techniques to better predict climate, visualizing the effects of extreme weather to create awareness, monitoring the emission of carbon with satellite imagery. The lectures will consist of discussions, reading papers, guest lecturers, and a final project. Prerequisite: CSC 242, or equivalent.
- Location
- Lechase Room 103
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CSC 461-1
Eustrat Zhupa
MW 3:25PM - 4:40PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
- Location
- Dewey Room 2162
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CSC 463-1
Fatemeh Nargesian
MW 9:00AM - 10:15AM
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This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL.
- Location
- Gavett Hall Room 312
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CSC 466-1
Christopher Kanan
MW 10:25AM - 11:40AM
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Deep learning has revolutionized many areas of artificial intelligence, including computer vision, natural language understanding, and more. This course reviews some of the latest advancements in deep learning, with a focus on methods that overcome the need for vast amounts of labeled data. Topics covered may include, but are not limited to, self-supervised learning in vision and language, self-training methods (e.g., student-teacher networks), continual learning, foundation models (e.g., Stable Diffusion, GPT-3, CLIP, etc), learning from biased and long-tailed datasets, and theory topics related to generalization in deep learning (e.g., The Lottery Ticket Hypothesis, Deep Double Descent, etc.). There are no formal requirements, but students are expected to know linear algebra, multivariate calculus, and have basic knowledge of deep learning, e.g, loss functions, optimization, regularization, multi-layer perceptron neural network architectures, etc.
- Location
- Meliora Room 205
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CSC 478-1
John Criswell
MW 6:15PM - 7:30PM
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This course will teach students the foundations of computer security. Students will learn what security is, the design principles of secure systems, how security is enforced, and how security is compromised. Topics include access controls, information flow, basic applications of cryptography, buffer overflow attacks, and malware. Prerequisites: CSC 252 or CSC 452 or ECE 200.
- Location
- Hylan Building Room 101
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CSC 480-1
Lane Hemaspaandra
MW 3:25PM - 4:40PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness. Prerequisites: CSC 173 and MTH 150. YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR THE MAIN COURSE
- Location
- Wegmans Room 1400
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CSC 480-10
Lane Hemaspaandra
W 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Hylan Building Room 306
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CSC 480-2
Lane Hemaspaandra
W 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Lattimore Room 210
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CSC 480-3
Lane Hemaspaandra
W 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Harkness Room 210
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CSC 480-4
Lane Hemaspaandra
R 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Hylan Building Room 105
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CSC 480-5
Lane Hemaspaandra
R 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Meliora Room 218
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CSC 480-7
Lane Hemaspaandra
W 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Meliora Room 218
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CSC 480-8
Lane Hemaspaandra
R 7:40PM - 8:55PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Bausch & Lomb Room 315
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CSC 480-9
Lane Hemaspaandra
R 3:25PM - 4:40PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
- Location
- Wegmans Room 1005
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CSC 482-1
Eustrat Zhupa
TR 9:40AM - 10:55AM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. Prerequisites: (CSC 172 and MTH 150) or MTH172. Students MUST register for a recitation when registering for this course.
- Location
- Gavett Hall Room 206
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CSC 482-2
Eustrat Zhupa
T 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
- Location
- Gavett Hall Room 301
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CSC 482-3
Eustrat Zhupa
W 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
- Location
- Gavett Hall Room 301
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CSC 482-4
Eustrat Zhupa
M 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
- Location
- Hylan Building Room 203
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CSC 482-5
Eustrat Zhupa
W 7:40PM - 8:55PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
- Location
- Hylan Building Room 203
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CSC 486-1
Lane Hemaspaandra
MW 2:00PM - 3:15PM
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The difference between computable and uncomputable problems and between feasible and infeasible problems. Regarding the latter, what properties of a problem make it computationally simple? What properties of a problem may preclude its having efficient algorithms? How computationally hard are problems? Complete sets and low information content; P=NP?; unambiguous computation and one-way functions; reductions relating the complexity of problems; complexity classes and hierarchies. Prerequisites: CSC 280
- Location
- Gavett Hall Room 301
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CSC 489-1
Anson Kahng
MW 4:50PM - 6:05PM
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This class will provide an introduction to topics at the intersection of computer science and economics, including game theory, auctions, incentive-compatible mechanism design, matching algorithms, human computation (e.g., crowdsourcing and peer prediction), trust and reputation systems, and social choice (voting) theory. We hope to convey the fact that the relationship between computer science and economics is a two-way street: It is important to consider incentives and strategic action are important when designing computation-intensive systems, and efficient and robust algorithms enable the deployment of impactful economic ideas. Prerequisites: Familiarity with probability theory, single-variable calculus, linear algebra, and basics of theoretical computer science. No background in economic theory is assumed or required. Students should also be prepared to write code in Python. Please email the instructor with any questions.
- Location
- Computer Studies Room 209
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CSC 491-1
Michael Scott
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CSC 494-1
John Criswell
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CSC 494-2
Chenliang Xu
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CSC 494-3
Lenhart Schubert
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CSC 494-4
Jiebo Luo
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CSC 494T-1
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CSC 495-01
James Allen
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CSC 495-02
John Criswell
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CSC 495-03
Zhen Bai
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CSC 495-04
Chen Ding
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CSC 495-05
Sandhya Dwarkadas
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CSC 495-06
Daniel Gildea
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CSC 495-07
Lane Hemaspaandra
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CSC 495-08
Ehsan Hoque
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CSC 495-09
Henry Kautz
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CSC 495-10
Jiebo Luo
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CSC 495-11
Fatemeh Nargesian
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CSC 495-12
Randal Nelson
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CSC 495-13
Sreepathi Pai
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CSC 495-14
Lenhart Schubert
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CSC 495-15
Michael Scott
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CSC 495-16
Daniel Stefankovic
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CSC 495-17
Muthuramakrishnan Venkitasubramaniam
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CSC 495-18
Chenliang Xu
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CSC 495-19
Yuhao Zhu
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CSC 495-20
Aaron White
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CSC 495-21
Thomas Howard
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CSC 495-22
Zhiyao Duan
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CSC 495A-1
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CSC 577-1
Chenliang Xu
TR 9:40AM - 10:55AM
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This is a seminar course with varying topics in the area of artificial Intelligence. May not be taught every year. This course covers advanced research topics in computer vision with an emphasis on learning structured representations and embeddings. Approaches for learning from unimodal (e.g., images and videos), and multimodal data (e.g., vision and language, vision and audio) will be covered and include topics from structured predications, deep learning and others. The course will be a mix of lecture, student presentation and discussion.
- Location
- Hylan Building Room 203
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CSC 579-1
Chen Ding
R 9:40AM - 10:55AM
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In recent years, runnable proofs have been shown successful for non-trivial theorems, algorithms, and programs. This seminar will use the Coq interactive theorem prover to learn, practice and explore the theory and applications of writing machine-checked proofs. Topics include functional programming, proof by induction, proof tactics, logic in Coq, propositions, and the Curry-Howard Correspondence. Students enrolling in the course are expected to have in-depth knowledge in either programming languages (CSC 253, 254 or 255) or mathematical logic.
- Location
- Lechase Room 103
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CSC 594-1
Sandhya Dwarkadas
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CSC 594-10
Michael Scott
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CSC 594-11
Daniel Gildea
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CSC 594-2
Henry Kautz
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CSC 594-3
Jiebo Luo
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CSC 594-4
Ji Liu
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CSC 594-5
Chenliang Xu
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CSC 594-6
Yuhao Zhu
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CSC 594-7
Ehsan Hoque
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CSC 594-8
Chen Ding
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CSC 594-9
Muthuramakrishnan Venkitasubramaniam
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CSC 595-1
John Criswell
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CSC 595-10
Jiebo Luo
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CSC 595-11
Fatemeh Nargesian
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CSC 595-12
Ji Liu
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CSC 595-13
Sreepathi Pai
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CSC 595-14
Lenhart Schubert
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CSC 595-15
Michael Scott
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CSC 595-16
Daniel Stefankovic
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CSC 595-17
Muthuramakrishnan Venkitasubramaniam
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CSC 595-18
Chenliang Xu
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CSC 595-19
Yuhao Zhu
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CSC 595-2
James Allen
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CSC 595-20
Aaron White
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CSC 595-21
Gonzalo Mateos Buckstein
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CSC 595-22
Thomas Howard
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CSC 595-23
Gregory Heyworth
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CSC 595-24
Mujdat Cetin
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CSC 595-3
Zhen Bai
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CSC 595-4
Chen Ding
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CSC 595-5
Sandhya Dwarkadas
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CSC 595-6
Daniel Gildea
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CSC 595-7
Lane Hemaspaandra
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CSC 595-8
Ehsan Hoque
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CSC 595-9
Henry Kautz
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CSC 595A-1
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CSC 595A-2
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CSC 595A-3
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CSC 595A-4
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CSC 595A-5
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CSC 595B-1
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CSC 595B-2
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CSC 597-1
M 11:50AM - 1:05PM
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- Wegmans Room 1400
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CSC 895-1
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CSC 897-1
Lenhart Schubert
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CSC 897A-1
Muthuramakrishnan Venkitasubramaniam
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CSC 897A-2
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CSC 899-01
James Allen
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CSC 899-02
John Criswell
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CSC 899-03
Zhen Bai
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CSC 899-04
Sandhya Dwarkadas
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CSC 899-05
Chen Ding
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CSC 899-06
Daniel Gildea
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CSC 899-07
Lane Hemaspaandra
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CSC 899-08
Ehsan Hoque
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CSC 899-09
Henry Kautz
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CSC 899-10
Jiebo Luo
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CSC 899-11
Fatemeh Nargesian
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CSC 899-12
Randal Nelson
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CSC 899-13
Sreepathi Pai
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CSC 899-14
Lenhart Schubert
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CSC 899-15
Michael Scott
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CSC 899-16
Daniel Stefankovic
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CSC 899-17
Muthuramakrishnan Venkitasubramaniam
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CSC 899-18
Chenliang Xu
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CSC 899-19
Yuhao Zhu
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CSC 899-20
Aaron White
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CSC 899A-1
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CSC 995-1
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CSC 997-02
Sandhya Dwarkadas
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CSC 997-03
Daniel Gildea
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CSC 997-04
Chen Ding
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CSC 997-05
John Criswell
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CSC 997-06
Muthuramakrishnan Venkitasubramaniam
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CSC 997-07
Lenhart Schubert
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CSC 997-08
Zhen Bai
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CSC 997-09
Chenliang Xu
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CSC 997-1
Michael Scott
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CSC 997-10
Sreepathi Pai
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CSC 997-11
Daniel Stefankovic
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Blank Description
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CSC 997-12
James Allen
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Blank Description
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CSC 997-13
Yuhao Zhu
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Blank Description
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CSC 997-14
Jiebo Luo
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Blank Description
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CSC 997-15
Ehsan Hoque
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Blank Description
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CSC 997-16
Henry Kautz
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Blank Description
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CSC 997A-1
Ehsan Hoque
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Blank Description
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CSC 997A-2
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Blank Description
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CSC 997A-3
Sreepathi Pai
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Blank Description
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CSC 999-01
James Allen
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Blank Description
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CSC 999-02
John Criswell
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Blank Description
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CSC 999-03
Zhen Bai
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Blank Description
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CSC 999-04
Chen Ding
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Blank Description
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CSC 999-05
Sandhya Dwarkadas
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Blank Description
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CSC 999-06
Daniel Gildea
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Blank Description
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CSC 999-07
Lane Hemaspaandra
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Blank Description
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CSC 999-08
Ehsan Hoque
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Blank Description
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CSC 999-09
Henry Kautz
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Blank Description
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CSC 999-10
Jiebo Luo
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Blank Description
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CSC 999-11
Fatemeh Nargesian
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Blank Description
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CSC 999-12
Ji Liu
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Blank Description
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CSC 999-13
Sreepathi Pai
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Blank Description
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CSC 999-14
Lenhart Schubert
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Blank Description
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CSC 999-15
Michael Scott
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Blank Description
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CSC 999-16
Daniel Stefankovic
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Blank Description
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CSC 999-17
Muthuramakrishnan Venkitasubramaniam
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Blank Description
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CSC 999-18
Chenliang Xu
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Blank Description
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CSC 999-19
Yuhao Zhu
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Blank Description
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CSC 999-20
Aaron White
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Blank Description
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CSC 999-21
Gonzalo Mateos Buckstein
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Blank Description
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